Pronunciation adaptation at the lexical level

There are various kinds of adaptation which can be used to enhance the performance of automatic speech recognizers. This paper is about pronunciation adaptation at the lexical level, i.e. about modeling pronunciation variation at the lexical level. In the early years of automatic speech recognition (ASR) research, the amount of pronunciation variation was limited by using isolated words. Since the focus gradually shifted from isolated words to conversational speech, the amount of pronunciation variation present in the speech signals has increased, as has the need to model it. This is reflected by the growing attention for this topic. In this paper, an overview of the studies on lexicon adaptation is presented. Furthermore, many examples are mentioned of situations in which lexicon adaptation is likely to improve the performance of speech recognizers. Finally, it is argued that some assumptions made in current standard ASR systems are not in line with the properties of the speech signals. Consequently, the problem of pronunciation variation at the lexical level probably cannot be solved by simply adding new transcriptions to the lexicon, as it is generally done at the moment.